CLARK 1.2.6.1 – Fast and Accurate Classification of Metagenomic and Genomic Sequences

CLARK 1.2.6.1

:: DESCRIPTION

Clark is a novel approach to classify metagenomic reads at the species or genus level with high accuracy and high speed.

::DEVELOPER

Algorithms and Computational Biology Lab ,University of California

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 CLARK

:: MORE INFORMATION

Citation

Ounit R, Wanamaker S, Close TJ, Lonardi S,
CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers
BMC Genomics 2015, 16:236.

MP3 1.0 – Prediction of Pathogenic Proteins in Metagenomic and Genomic Datasets

MP3 1.0

:: DESCRIPTION

MP3 is a standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets.

::DEVELOPER

MetaBioSys laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 MP3

:: MORE INFORMATION

Citation

MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.
Gupta A, Kapil R, Dhakan DB, Sharma VK.
PLoS One. 2014 Apr 15;9(4):e93907. doi: 10.1371/journal.pone.0093907.

MGKit 0.4.2 – Metagenomic Framewotk for the Study of Microbial Communities

MGKit 0.4.2

:: DESCRIPTION

MGKit library is to provide a series of useful modules and packages to make it easier to build custom pipelines for metagenomics or any kind of bioinformatics analysis.

::DEVELOPER

The Creevey Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

MGKit

:: MORE INFORMATION

Citation

Rubino, F. and Creevey, C.J. 2014.
MGkit: Metagenomic Framework For The Study Of Microbial Communities. 
figshare [doi:10.6084/m9.figshare.1269288].`

MBBC 1.1 – Metagenomic Binning Based on Composition

MBBC 1.1

:: DESCRIPTION

MBBC is a useful tool in metagenomic studies. It is a novel composition-based approach to bin environmental shotgun reads, by considering the k-mer frequency in reads and the inferred Markovian property of the unknown species or OTUs (operational taxonomic units).

::DEVELOPER

Hu Lab – Data Integration and Knowledge Discovery @ UCF

:: SCREENSHOTS

MBBC

:: REQUIREMENTS

  • Linux / WIndows/ MacOsX
  • Java

:: DOWNLOAD

 MBBC

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2015 Feb 5;16(1):36. [Epub ahead of print]
MBBC: an efficient approach for metagenomic binning based on clustering.
Wang Y, Hu H, Li X.

MUSiCC – Metagenomic Universal Single-Copy Correction

MUSiCC

:: DESCRIPTION

MUSiCC is a software package for normalizing and correcting gene abundance measurements derived from metagenomic shotgun sequencing

::DEVELOPER

the Borenstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab / Python

:: DOWNLOAD

 MUSiCC

 :: MORE INFORMATION

Citation

MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.
Manor O, Borenstein E.
Genome Biol. 2015 Mar 25;16:53. doi: 10.1186/s13059-015-0610-8.

MetaComp – Metagenomic Comparative Analysis Platform

MetaComp

:: DESCRIPTION

 MetaComp is a comparative analysis platform for metagenomic data, which contains multiple tools for

::DEVELOPER

ZhuLab, Peking Uiniversity, Beijing

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows
  • R

:: DOWNLOAD

 MetaComp

:: MORE INFORMATION

MGAviewer 1.3.0 – MetaGenomic Alignment Viewer

MGAviewer 1.3.0

:: DESCRIPTION

MGAviewer is a graphic interface for visualization of alignment results between metagenomic samples and reference microbial genomes.

::DEVELOPER

Group of Weizhong Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MGAviewer

:: MORE INFORMATION

LikelyBin 0.1 – Metagenomic Binner

LikelyBin 0.1

:: DESCRIPTION

LikelyBin is an unsupervised metagenomic binner.

::DEVELOPER

WeitzGroup@GeorgiaTech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

LikelyBin ; Test data

:: MORE INFORMATION

Citation:

Andrey Kislyuk, Srijak Bhatnagar, Jonathan Dushoff and Joshua S. Weitz. Unsupervised Statistical Clustering of Environmental Shotgun Sequences. BMC Bioinformatics 2009, 10:316.